Group latent factor model for recommendation with multiple user behaviors

	Group latent factor model for recommendation with multiple user behaviors
	Cheng, Jian; Yuan, Ting; Wang, Jinqiao; Lu, Hanqing
	2014
会议名称	SIGIR 2014 - the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
会议录名称	International ACM SIGIR Conference on Research and Development in Information Retrieval
页码	995-998
会议日期	2014
会议地点	Gold Coast, Queensland，Australia
摘要	Recently, some recommendation methods try to relieve the data sparsity problem of Collaborative Filtering by exploiting data from users’ multiple types of behaviors. However, most of the exist methods mainly consider to model the correlation between different behaviors and ignore the heterogeneity of them, which may make improper information transferred and harm the recommendation results. To address this problem, we propose a novel recommendation model, named Group Latent Factor Model (GLFM), which attempts to learn a factorization of latent factor space into subspaces that are shared across multiple behaviors and subspaces that are specific to each type of behaviors. Thus, the correlation and heterogeneity of multiple behaviors can be modeled by these shared and specific latent factors. Experiments on the real-world dataset demonstrate that our model can integrate users’ multiple types of behaviors into recommendation better.
关键词	Group Latent Factor Model Recommendation Multiple User Behaviors
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/4677
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Wang, Jinqiao
推荐引用方式 GB/T 7714	Cheng, Jian,Yuan, Ting,Wang, Jinqiao,et al. Group latent factor model for recommendation with multiple user behaviors[C],2014:995-998.

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